When we want to measure the effects of an intervention or service, it’s tempting to start by asking "how much?" — jumping straight to measuring outcomes without thinking about what else besides the intervention may influence our results. The causal model of health and wellbeing coaching we have developed through countless discussions with expert practitioners allows us to a different set of questions: those that begin with "what if?". (For more details on the model, see here.)
Much like with a pharmaceutical intervention, where the precise effect of administering a drug depends on diverse factors such as dosage, adherence, interactions with other medications, and so on, so too is the effect of health and wellbeing coaching influenced by a wide range of factors. Our causal model captures these different factors and their relationships, showing, for example, that the gender of the coachee (an aspect of the coachee position) can directly affect both the coaching itself and the outcomes we’re interested in. People differ in how they respond to coaching, and also in how confident and adept they are in managing their own health and wellbeing; the model proposes that these differences are partially determined, to a greater or lesser degree, by gender.
Having identified these effects, we can now ask a "what-if?" question: What if all of the coachees were of the same gender, keeping everything else the same? How would that change the outcomes? As long as there are enough coachees in our dataset who have the same values for all model inputs except for their gender, we can estimate the effect of gender by comparing the difference in average outcomes for these two groups. This difference is equivalent to the change in average outcomes we would see if everyone was of the same gender.
The value of asking "what if?"
Asking the what-if? question about gender gives us two different types of insight:
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Knowing the estimated effect of gender on outcomes allows us to get a better estimate of the direct effect of health and wellbeing coaching by identifying and removing a source of bias. Previously, we didn’t know how much of the outcomes we observe is due to the coaching and how much of it is due to gender effects; now we can remove gender effects from the equation and get a less biased estimate of the coaching effects. Asking "what if?" helps us answer the "how much?".
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The estimated effect of gender may suggest ways in which a health and wellbeing coaching service can be improved. For example, let’s imagine we find that, all else being equal, the effect of gender on outcomes is negligible (i.e. people of different genders are equally likely to experience positive outcomes), but more women are being referred for or attend coaching than men. We can use the insight about equality of outcomes to encourage more referrals for men, and to encourage more men to attend coaching. Being able to demonstrate that coaching works just as well for people like them as for anyone else can help break down some of the barriers that lead people to assume that coaching is "not for me".
The same approach works for any input variable in our model (i.e. any aspect of the coachee position, the coach position, or the coaching modality). What if every coaching session was face-to-face rather than remote? What if every coachee was coached by someone who was experienced in working with people with their specific needs? Answering each of these questions gives us more insight into the direct effects of coaching and may point to opportunities to improve health and wellbeing coaching services. With data from a small number of coachees, we might only be able to identify some of the what-if? effects, getting closer to an unbiased estimate of the coaching effect but still leaving many unknowns. As our dataset gets larger, however, we can resolve more and more of the what-if? scenarios.
Changing the question
It’s easy to reduce impact measurement to a simple sizing up of outcomes, but there’s much more to be learnt by thinking about the various causes of those outcomes, and what would happen if those causes were shaped differently. Drawing on the knowledge of expert practitioners to build a causal model of our intervention can allow us to explore the different effects that lead to real-life outcomes, using what-if? questions to estimate the size of those effects.
Asking "what if?" also helps us improve health and wellbeing coaching services by identifying levers we can pull and what their effects are likely to be — for example if we made changes to the way the coaching is delivered, or targeted different underserved populations for coaching support.
What are the what-if questions you would want to ask about factors affecting your own work?

